**Description of Activities: **Research is conducted in the areas of information theory, communication theory, stochastic networked control systems, multi-user communication networks, data compression, error-control coding, joint source-channel coding, team decision and game theory, applied probability, Bayesian methods, statistical pattern recognition, machine learning, time-series analysis and signal processing.

Full-Time Faculty | Research Interests |
---|---|

Fady Alajaji | Information and communication theory, source-channel coding, data compression, digital communications, applied probability |

Tamás Linder | Information and communication theory, source-channel coding, vector quantization, statistical pattern recognition and machine learning |

Glen Takahara |
Communication networks, queuing systems, Bayesian methodology |

David J. Thomson | Statistical communications, signal processing, time series and spectrum estimation, global warming, space physics |

Serdar Yüksel | Stochastic control theory, stochastic dynamical systems, networked control, information theory, source coding and quantization |

Affiliated Faculty | Primary Affiliation | Research Interests |
---|---|---|

Steven D. Blostein | Dept. of Electrical and Computer Engineering, Queen's University | Wireless communications; smart antennas; signal processing; multi-user communications |

Navin Kashyap | Adjunct Professor, Dept. of Electrical and Computer Engineering, Indian Institute of Science | Discrete applied mathematics; coding for data communication and storage; source coding; data synchronization; information theory; symbolic dynamics |

**Graduate Courses Offered:**

Math 800 - Seminar

Math 806 - Introduction to Coding Theory

Math 834 - Optimization Theory and Applications

Math 872 - Control of Stochastic Systems

Math 874 - Information Theory

Math 877 - Data Compression and Source Coding

Stat 855 - Stochastic Processes and Applications

Stat 864 - Discrete Time Series Analysis

**Application for Graduate Studies:** Queen's provides an ideal environment to do graduate study in Mathematics and Engineering, Applied Mathematics or Mathematics or Statistics. Our course curriculum is rigorous and increasingly diverse. The applicants should follow the guidelines listed on our Application Information page. Typically students with backgrounds in mathematics, applied mathematics, electrical and computer engineering and systems engineering with strong interests in mathematical sciences will find the graduate program very stimulating and rewarding.

Our research environment is enhanced by a very active group of graduate students. There is always a steady stream of visiting scholars and post-doctoral fellows.